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United States President Joe Bidens administration as well as many bipartisan US legislators and advocates have said they want the Authorization for Use of Military Force Against Iraq (AUMF)repealed.

The authorisation was signed by former President George W Bush in 2002, enabling the US invasion and occupation of Iraq as the USs two-decade war on terror went into full swing. It has increasingly been condemned by critics for giving the US executive branch broad and menacingly vague military powers.

On Thursday, a group of bipartisan legislators in both the House and Senate launched their latest effort to do away with the 2002 law, reintroducing a bill to repeal the authorisation.

This attempt follows a period between 2021 and 2022 that advocates said represented the best opportunity yet to pass a repeal. However, the path has likely narrowed with Republicans taking control of the House of Representatives following last years midterm elections.

All of these groups are saying enough is enough. Get this appeal off the books. Put Congress back in the business of making that hard decision about when we go to war, Heather Brandon-Smith, the legislative director for Militarism and Human Rights at Friends Committee on National Legislation (FCNL), a Washington lobby group, told Al Jazeera.

She noted that the 20th anniversary of the 2003 US invasion of Iraq was coming up in March.

People across the political divide seem to really want to see Congress making the decision and not the president deciding when, where and against whom the US goes to war, she said. That hasnt changed.

Critics have said the AUMFs reason for being became increasingly dubious after the US officially ended, in 2011, its combat operations in Iraq which saw US troops in the country surge to a peak of 170,000 as well as combat operations there against ISIS (ISIL) in 2021.

The repeal of the 2002 AUMF along with reformation of the geographically broader and more politically fraught 2001 AUMF, which allows the US executive to pursue military action against individuals or groups deemed connected to the 9/11 attacks have been at the centre of efforts to restructure the legal architecture that has guided US military action abroad in recent decades.

The US Congress, which has the sole constitutional power to declare war, has not done so since 1941 when it approved declarations against Japan in the wake of the Pearl Harbour attacks and, days later, against Nazi-controlled Germany and axis-allied Italy.

Instead, to involve the US military in conflict abroad, presidential administrations have relied on Article 2 of the US Constitution, which grants limited war powers to the executive branch, and legislation passed by Congress usually the so-called Authorizations of Use of Military Force (AUMFs).

AUMFs authorise major war, according to Scott Anderson, a senior fellow at Columbia Law Schools National Security Law Program. They provide legal and political cover amid lingering questions over the limits of a presidents constitutional war powers and, most significantly, cover for questions over whether presidents can take action that risks a major war without congressional authorisation.

The 2002 AUMF, at least in regards to things that intersect with Iraq, opens up the possibility of the president being able to lean on it and initiate a major war without really having to go back and check with or ensure they have the support of the most democratic branch of government Congress or just, kind of, more generally, a broader political support, Anderson said.

Now, are our presidents going to do that routinely? No, theyre not. But there are circumstances where they might.

Most recently, the administration of Former President Donald Trump used the 2002 Iraq AUMF, in part, to justify the deadly drone strike on Iranian General Qassem Soleimani on the outskirts of the Iraqi capital Baghdad in early 2020.

The killing led to US-Iran sabre rattling that risked escalating into full-fledged war.

The Biden administration has said it does not rely on the 2002 AUMF to solely justify any of its military actions in Iraq.

Anderson, who previously served as the legal adviser for the US embassy in Baghdad, noted that despite this, Iraq remains a particularly significant arena when it comes to the potential for wider escalation. That is largely due to the presence of Iran-aligned militias in Iraq, Irans outsized involvement in its neighbour and ongoing political and economic crises.

The US has 2,000 troops in Iraq, operating in advisory roles. Foreign forces are regularly targeted by armed groups calling for their removal.

Meanwhile, Anderson said, the executive branch in recent years has articulated an interpretation of the 2002 Iraq AUMF that allows the president to use military force in combating terrorists in the country or addressing any sort of threat to a stable government.

This creates several possible paths to escalation under a future administration, he said.

The US relationship with Iran, I think, is one of those very challenging ones, where you could see a particular president feeling liberated by the 2002 AUMF, taking riskier action, or pushing the envelope more in terms of fighting Iran.

Repeal of the 2002 AUMF has had uniquely bipartisan support in Congress in recent years, with a standalone bill introduced in 2021 by Representative Barbara Lee passing the Democrat-controlled House with the support of 49 Republicans.

While introducing the most recent legislation, which would also repeal the 1991 AUMF that authorised the USs involvement in the Gulf War, Lee said it was far past time to put decisions of military action back in the hands of the people, as the Constitution intended.

Past congressional efforts have made for some interesting bedfellows, with several Trump-aligned legislators in the Republican Partys farthest-right reaches including Representatives Matt Gaetz, Marjorie Taylor Greene and Lauren Boebert joining the Democratic majority in pursuit of repeal.

In 2021 in the Senate, Tim Kaine, a Democrat, and Todd Young, a Republican, also introduced a stand-alone bill that went on to gain 11 Republican co-sponsors, making it poised to overcome the 60-vote threshold needed to avoid a filibuster in a congressional session where Democrats still controlled both chambers.

Kaine and Young have again teamed up in introducing the newest legislation in the Senate.

In 2021, Senate Majority Leader Chuck Schumer also gave his full-throated support for the repeal, promising to bring the bill to a vote and, with the Biden administration giving its approval to the effort, the course appeared to be charted.

Nevertheless, a Senate floor vote on the standalone repeal never came to pass, likely due to concerns over how much limited floor-time debate over the legislation would eat up, according to analysts. While Senators Kaine and Young sought to include an amendment to the Senate version of the 2023 NDAA as was approved in the House the effort was unsuccessful.

In the waning days of 2022, anti-war groups made a last-minute appeal to Schumer.

In repealing the 2002 Iraq AUMF whether by standalone vehicle or through the omnibus spending package Congress would finally reclaim its constitutional war powers in a manner both deeply significant and increasingly uncontroversial, 37 groups said in a letter to the top Democrat.

We urge you to seize this opportunity to get it off the books for good.

Analysts and advocates have said despite new obstacles, hope remains in the new congressional term, with Democrats maintaining a 51-seat majority in the Senate and Republicans taking 222 seats in the House, giving them a slight majority over Democrats 212.

In the Senate, all 11 Republican co-sponsors of the 2022 repeal bill remain in office, while 40 of the 49 Republicans who supported the House bill in 2021 have kept their seats.

Still, observers have said it remains unlikely House Republicans would bring such legislation to a vote, with large portions of the Republican Party remaining opposed.

That means pressure would almost surely have to come from Senate, with FCLNs Brandon-Smith saying the best chance would likely be including repeal as an amendment to so-called must pass legislation, such as an NDAA or other omnibus spending packages.

Despite the missed opportunities for repeal last year, she struck an optimistic tone.

The fact is that there are still bipartisan majorities in both the House and the Senate who want to see this AUMF off the books So we are still in quite a strong position when it comes to support in Congress, she said, which provides opportunities.

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Why is US repeal of Iraq war authorisation still relevant?

Iraq's Ala Bashir explores human ability to forget, forgive and heal in Dubai exhibition  The National

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Iraq's Ala Bashir explores human ability to forget, forgive and heal in Dubai exhibition - The National

The Super Bowl of chess in N.J.? Thats the vision for one local expert player  NJ.com

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The Super Bowl of chess in N.J.? Thats the vision for one local expert player - NJ.com

Many see the slogan Black Lives Matter as a plea to secure the right to life, liberty, and the pursuit of happiness for all Americans, especially historically wronged African Americans. They add the BLM hashtag to their social-media profiles, carry BLM signs at protests, and make financial donations.

Tragically, when they do donate, they are likely to bankroll a number of radical organizations,founded by committed Marxists whose goals arent to make the American Dream a reality for everyonebut to transform America completely.

This might be unknown to some of the worlds best-known companies, which have jumped on the BLM bandwagon. Brands like Airbnb and Spanx have promised direct donations.

True, others like Nike and Netflix have shrewdly channeled their donations elsewhere, like the NAACP and other organizations that have led the struggle for civil rights for decades. These companies are likely aware of BLMs extreme agenda and recoil from bankrolling destructive ideas. But it requires sleuthing to learn this.

Companies that dont do this hard work are providing air cover for a destructive movement and compelling their employees, shareowners and customers to endorse the same. Just ask BLM leaders Alicia Garza, Patrisse Cullors and Opal TometiIn a revealing 2015 interview, Cullors said, Myself and Alicia in particular are trained organizers. We are trained Marxists. That same year, Tometi was hobnobbing with Venezuelas Marxist dictator Nicols Maduro, of whose regime she wrote: In these last 17 years, we have witnessed the Bolivarian Revolution champion participatory democracy and construct a fair, transparent election system recognized as among the best in the world.

Millions of Venezuelans suffering under Maduros murderous misrule presumably couldnt be reached for comment.

Visit the Black Lives Matter website, and the first frame you get is a large crowd with fists raised and the slogan Now We Transform.Read the list of demands, and you get a sense of how deep a transformation they seek.

One proclaims: We disrupt the Western-prescribed nuclear-family-structure requirement by supporting each other as extended families and villages that collectively care for one another.

A partner organization, the Movement for Black Lives, or M4BL, calls for abolishing all police and all prisons. It also calls for a progressive restructuring of tax codes at the local, state and federal levels to ensure a radical and sustainable redistribution of wealth.

Another M4BL demand is the retroactive decriminalization, immediate release and record expungement of all drug-related offenses and prostitution and reparations for the devastating impact of the war on drugs and criminalization of prostitution.

This agenda isnt what most people signed up for when they bought their Spanx or registered for Airbnb. Nor is it what most people understood when they expressed sympathy with the slogan that Black Lives Matter.

Garza first coined the phrase in a July 14, 2013, Facebook post the day George Zimmerman was acquitted of murdering Trayvon Martin. Her friend Cullors put the hashtag in front and joined the words, so it could travel through social media. Tometi thought of creating an actual digital platform, BlackLivesMatter.com.

The group became a self-styled global network in 2014 and a fiscally sponsored project of a separate progressive nonprofit in 2016, according to Robert Stilson of the Capital Research Center. This evolution has helped embolden an agenda vastly more ambitious than just #DefundthePolice.

The goals of the Black Lives Matter organization go far beyond what most people think. But they are hiding in plain sight, there for the world to see, if only we read beyond the slogans and the innocuous-sounding media accounts of the movement.

The groups radical Marxist agenda would supplant the basic building block of societythe familywith the state and destroy the economic system that has lifted more people from poverty than any other. Black lives, and all lives, would be harmed.

Theirs is a blueprint for misery, not justice. It must be rejected.

Continued here:
The Agenda of Black Lives Matter Is Far Different From the Slogan

These terms are often used interchangeably, but what are the differences that make them each a unique technology?

Technology is becoming more embedded in our daily lives by the minute, and in order to keep up with the pace of consumer expectations, companies are more heavily relying on learning algorithms to make things easier. You can see its application in social media (through object recognition in photos) or in talking directly todevices (like Alexa or Siri).

These technologies are commonly associated with artificial intelligence, machine learning, deep learning, and neural networks, and while they do all play a role, these terms tend to be used interchangeably in conversation, leading to some confusion around the nuances between them. Hopefully, we can use this blog post to clarify some of the ambiguity here.

Perhaps the easiest way to think about artificial intelligence, machine learning, neural networks, and deep learning is to think of them like Russian nesting dolls. Each is essentially a component of the prior term.

That is, machine learning is a subfield of artificial intelligence. Deep learning is a subfield of machine learning, and neural networks make up the backbone of deep learning algorithms. In fact, it is the number of node layers, or depth, of neural networks that distinguishes a single neural network from a deep learning algorithm, which must have more than three.

Neural networksand more specifically, artificial neural networks (ANNs)mimic the human brain through a set of algorithms. At a basic level, a neural network is comprised of four main components: inputs, weights, a bias or threshold, and an output. Similar to linear regression, the algebraic formula would look something like this:

From there, lets apply it to a more tangible example, like whether or not you should order a pizza for dinner. This will be our predicted outcome, or y-hat. Lets assume that there are three main factors that will influence your decision:

Then, lets assume the following, giving us the following inputs:

For simplicity purposes, our inputs will have a binary value of 0 or 1. This technically defines it as a perceptron as neural networks primarily leverage sigmoid neurons, which represent values from negative infinity to positive infinity. This distinction is important since most real-world problems are nonlinear, so we need values which reduce how much influence any single input can have on the outcome. However, summarizing in this way will help you understand the underlying math at play here.

Moving on, we now need to assign some weights to determine importance. Larger weights make a single inputs contribution to the output more significant compared to other inputs.

Finally, well also assume a threshold value of 5, which would translate to a bias value of 5.

Since we established all the relevant values for our summation, we can now plug them into this formula.

Using the following activation function, we can now calculate the output (i.e., our decision to order pizza):

In summary:

Y-hat (our predicted outcome) = Decide to order pizza or not

Y-hat = (1*5) + (0*3) + (1*2) - 5

Y-hat = 5 + 0 + 2 5

Y-hat = 2, which is greater than zero.

Since Y-hat is 2, the output from the activation function will be 1, meaning that we will order pizza (I mean, who doesn't love pizza).

If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. Now, imagine the above process being repeated multiple times for a single decision as neural networks tend to have multiple hidden layers as part of deep learning algorithms. Each hidden layer has its own activation function, potentially passing information from the previous layer into the next one. Once all the outputs from the hidden layers are generated, then they are used as inputs to calculate the final output of the neural network. Again, the above example is just the most basic example of a neural network; most real-world examples are nonlinear and far more complex.

The main difference between regression and a neural network is the impact of change on a single weight. In regression, you can change a weight without affecting the other inputs in a function. However, this isnt the case with neural networks. Since the output of one layer is passed into the next layer of the network, a single change can have a cascading effect on the other neurons in the network.

See this IBM Developer article for a deeper explanation of the quantitative concepts involved in neural networks.

While it was implied within the explanation of neural networks, its worth noting more explicitly. The deep in deep learning is referring to the depth of layers in a neural network. A neural network that consists of more than three layerswhich would be inclusive of the inputs and the outputcan be considered a deep learning algorithm. This is generally represented using the following diagram:

Most deep neural networks are feed-forward, meaning they flow in one direction only from input to output. However, you can also train your model through backpropagation; that is, move in opposite direction from output to input. Backpropagation allows us to calculate and attribute the error associated with each neuron, allowing us to adjust and fit the algorithm appropriately.

As we explain in our Learn Hub article on Deep Learning, deep learning is merely a subset of machine learning. The primary ways in which they differ is in how each algorithm learns and how much data each type of algorithm uses. Deep learning automates much of the feature extraction piece of the process, eliminating some of the manual human intervention required. It also enables the use of large data sets, earning itself the title of "scalable machine learning" in this MIT lecture. This capability will be particularly interesting as we begin to explore the use of unstructured data more, particularly since 80-90% of an organizations data is estimated to be unstructured.

Classical, or "non-deep", machine learning is more dependent on human intervention to learn. Human experts determine the hierarchy of features to understand the differences between data inputs, usually requiring more structured data to learn. For example, let's say that I were to show you a series of images of different types of fast food, pizza, burger, or taco. The human expert on these images would determine the characteristics which distinguish each picture as the specific fast food type. For example, the bread of each food type might be a distinguishing feature across each picture. Alternatively, you might just use labels, such as pizza, burger, or taco, to streamline the learning process through supervised learning.

"Deep" machine learning can leverage labeled datasets, also known as supervised learning, to inform its algorithm, but it doesnt necessarily require a labeled dataset. It can ingest unstructured data in its raw form (e.g. text, images), and it can automatically determine the set of features which distinguish "pizza", "burger", and "taco" from one another.

For a deep dive into the differences between these approaches, check out "Supervised vs. Unsupervised Learning: What's the Difference?"

By observing patterns in the data, a deep learning model can cluster inputs appropriately. Taking the same example from earlier, we could group pictures of pizzas, burgers, and tacos into their respective categories based on the similarities or differences identified in the images. With that said, a deep learning model would require more data points to improve its accuracy, whereas a machine learning model relies on less data given the underlying data structure. Deep learning is primarily leveraged for more complex use cases, like virtual assistants or fraud detection.

For further info on machine learning, check out the following video:

Finally, artificial intelligence (AI) is the broadest term used to classify machines that mimic human intelligence. It is used to predict, automate, and optimize tasks that humans have historically done, such as speech and facial recognition, decision making, and translation.

There are three main categories of AI:

ANI is considered weak AI, whereas the other two types are classified as strong AI. Weak AI is defined by its ability to complete a very specific task, like winning a chess game or identifying a specific individual in a series of photos. As we move into stronger forms of AI, like AGI and ASI, the incorporation of more human behaviors becomes more prominent, such as the ability to interpret tone and emotion. Chatbots and virtual assistants, like Siri, are scratching the surface of this, but they are still examples of ANI.

Strong AI is defined by its ability compared to humans. Artificial General Intelligence (AGI) would perform on par with another human while Artificial Super Intelligence (ASI)also known as superintelligencewould surpass a humans intelligence and ability. Neither forms of Strong AI exist yet, but ongoing research in this field continues. Since this area of AI is still rapidly evolving, the best example that I can offer on what this might look like is the character Dolores on the HBO show Westworld.

While all these areas of AI can help streamline areas of your business and improve your customer experience, achieving AI goals can be challenging because youll first need to ensure that you have the right systems in place to manage your data for the construction of learning algorithms. Data management is arguably harder than building the actual models that youll use for your business. Youll need a place to store your data and mechanisms for cleaning it and controlling for bias before you can start building anything. Take a look at some of IBMs product offerings to help you and your business get on the right track to prepare and manage your data at scale.

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AI vs. Machine Learning vs. Deep Learning vs. Neural ... - IBM